BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//ACC//Meetings//EN BEGIN:VTIMEZONE TZID:Eastern Standard Time BEGIN:STANDARD DTSTART:16011101T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:Eastern Standard Time END:STANDARD BEGIN:DAYLIGHT DTSTART:16010301T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:Eastern Daylight Time END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:\n\n\nWebinar Overview\nJoin us for this ACC Cross-Sectional ar tificial intelligence (AI) Work Group Webinar where we discuss AI in clini cal practice.\nIn this ACC Cross-Sectional AI Work Group webinar\, we will discuss AI in clinical practice by utilizing a major reporting guideline from the EQUATOR Network: the recently published TRIPOD-AI (Transparent Re porting of a multivariable prediction model for Individual Prognosis Or Di agnosis – AI update). TRIPOD-AI offers harmonized guidance for reporting s tudies that develop or validate prediction models using either regression or machine learning methods. The aim of these guidelines is to support tra nsparent reporting\, facilitate critical appraisal and enhance the impleme ntation of these models. Gary J. Collins\, MD\, will present this key new document\, offering insights into its development and the challenges encou ntered along the way. Following his presentation\, two early-career AI res earchers will showcase two AI-based studies in pediatric cardiology\, illu strating how the TRIPOD-AI statement can be applied in practice.\n\nKey Ob jectives/Learning Outcomes: By the end of this webinar\, participants will be able to:\n\nUnderstand the key components and rationale behind the TRI POD-AI statement.\nGain awareness of the critical elements for transparent and high-quality reporting of AI-based prediction models.\nLearn how to a ccess TRIPOD-AI resources\, including the dedicated checklist.\nBe able to apply TRIPOD-AI principles to improve the reporting quality of their own studies.\n\n\nWebinar Recording\n\n\n\n\nWebinar Participants\nGary J. Col lins\, MD\nOrkun Baloglu\, MD\nIzzet Turkalp Akbasli\, MD\nAddison Gearhar t\, MD\nFrancesca Sperotto\, MD\, PhD\nAnthony C. Chang\, MD\nMichael Satz er\, MD\n\nCredit Information\nNo credit is being offered for this webinar .\n\n\n\n\n\n .videoWrapper {\n position: relative\;\n padding-bo ttom: 56.25%\; /* 16:9 */\n padding-top: 25px\;\n height: 0\;\n } \n .videoWrapper iframe {\n position: absolute\;\n top: 0\;\n left: 0\;\n width: 100%\;\n height: 100%\;\n }\n DTEND;TZID=Eastern Standard Time:20250924T121500 DTSTAMP:20260410T071015Z DTSTART;TZID=Eastern Standard Time:20250924T120000 LOCATION:Online - Eastern Time (ET) SEQUENCE:0 SUMMARY:AI in Clinical Practice UID:85d83237-8b3b-454a-a6eb-de478ca2a814@acc.org X-ALT-DESC;FMTTYPE=text/html:
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Webinar Overvi ew

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Join us for this ACC Cross-Sectional ar tificial intelligence (AI) Work Group Webinar where we discuss AI in clini cal practice.

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In this ACC Cross-Sectional AI Work G roup webinar\, we will discuss AI in clinical practice by utilizing a majo r reporting guideline from the EQUATOR Network: the recently published TRI POD-AI (Transparent Reporting of a multivariable prediction model for Indi vidual Prognosis Or Diagnosis – AI update). TRIPOD-AI offers harmonized gu idance for reporting studies that develop or validate prediction models us ing either regression or machine learning methods. The aim of these guidel ines is to support transparent reporting\, facilitate critical appraisal a nd enhance the implementation of these models. Gary J. Collins\, M D\, will present this key new document\, offering insights into i ts development and the challenges encountered along the way. Following his presentation\, two early-career AI researchers will showcase two AI-based studies in pediatric cardiology\, illustrating how the TRIPOD-AI statemen t can be applied in practice.

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Key Objecti ves/Learning Outcomes: By the end of this webinar\, participants will be able to:

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  1. Understand the key componen ts and rationale behind the TRIPOD-AI statement.
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  2. Gain awareness of the critical elements for transparent and high-quality reporting of AI- based prediction models.
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  3. Learn how to access TRIPOD-AI resources \, including the dedicated checklist.
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  4. Be able to apply TRIPOD-AI principles to improve the reporting quality of their own studies.
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    Webinar Recording

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    Webinar Participants

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    Gary J. Collins\, MD
    \nOrkun Baloglu\, MD
    \nIzzet Turkalp Akbasli\, MD
    \nAddison Gear hart\, MD
    \nFrancesca Sperotto\, MD\, PhD
    \nAnthony C. Chang\, M D
    \nMichael Satzer\, MD

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    Credit Infor mation

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    No credit is being offered for this webinar.

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